Adaptive Unit Vector Mapping for Low-Distortion Volumetric Compression
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Solution Overview
Problem
Visual volumetric content captured by sensors like LIDAR systems and 3-D cameras contains large amounts of data, including unit vectors such as normal and tangent vectors, which is costly and time-consuming to store and transmit due to the distortion and discontinuities introduced when mapping three-dimensional unit vectors to a two-dimensional planar representation.
Innovation Solution
Adaptive selection of mappings to map three-dimensional unit vectors onto a planar representation of a unit sphere, minimizing distortion and discontinuities by positioning points near the origin and avoiding distorted regions, combined with entropy encoding of residual values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If three-dimensional unit vectors are mapped to a two-dimensional planar representation, then data storage and transmission become more efficient, but distortion and discontinuities are introduced
Solution Approach 1:
The patent segments the unit sphere into multiple triangular zones and processes unit vectors differently based on their location. Vectors in distorted regions are mapped to alternative representations, while vectors in low-distortion regions use standard mapping. This segmentation allows the system to achieve efficient 2D compression while maintaining vector accuracy by avoiding distorted regions.
Solution Approach 2:
The patent applies different mapping strategies to different local regions of the unit sphere. Rather than using a uniform mapping approach, the system identifies regions with high distortion and applies alternative representations specifically to those areas. This local quality approach ensures that each region is handled optimally, minimizing overall distortion while achieving compression.
2Device complexity
If all unit vectors are compressed using the same mapping method, then processing is simpler, but regions with high distortion suffer from significant quality loss
Solution Approach 1:
The patent implements a dynamic mapping selection process where the system adaptively chooses the appropriate representation method based on the location and characteristics of each unit vector. Rather than using a static, uniform mapping approach, the system dynamically switches between different mapping strategies to optimize both processing efficiency and vector accuracy for each specific case.
Solution Approach 2:
The patent changes the representation parameters of unit vectors based on their location on the unit sphere. For vectors in regions where standard mapping causes high distortion, the system alters the representation parameters by using alternative mappings or triangular zone representations. This parameter change approach allows the system to maintain processing simplicity while significantly improving vector representation accuracy in problematic regions.
3Manufacturing precision
If unit vectors are stored in three-dimensional format, then accuracy is maintained, but storage costs and transmission time increase significantly
Solution Approach 1:
The patent transforms three-dimensional unit vectors into a two-dimensional planar representation, achieving dimensionality reduction. By mapping vectors from 3D space to a 2D plane, the system reduces the data volume required for storage and transmission while implementing correction mechanisms to maintain acceptable accuracy levels. This dimensionality change is the core approach to improving transmission efficiency.
Solution Approach 2:
The patent introduces an intermediary mapping process between the original 3D unit vectors and their compressed representation. This intermediary step involves projecting vectors onto the unit sphere, dividing the sphere into triangular zones, and applying appropriate mapping strategies. The intermediary representation allows the system to bridge the gap between high-accuracy 3D storage and efficient 2D compression, achieving both goals simultaneously.
Data Source
AI summary
A system compresses and decompresses attribute information for visual volumetric content, such as a mesh representation. Attribute values are included in the visual volumetric representation, wherein at least some of the attribute values include unitary vectors, such as surface normal vectors or surface tangent vectors having a magnitude of one unit. In order to compress the attribute information the three-dimensional unit vectors are mapped into two dimensional parametric coordinates for a planar representation of a unit sphere. To reduce negative effects on compression due to distortion or discontinuities in the planar representation, mappings for compressing respective unit vectors are adaptively selected.